BMC Med Inform Decis Mak - Decision tree-based learning to predict patient controlled analgesia consumption and readjustment.

Tópicos

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Resumo

CKGROUND: Appropriate postoperative pain management contributes to earlier mobilization, shorter hospitalization, and reduced cost. The under treatment of pain may impede short-term recovery and have a detrimental long-term effect on health. This study focuses on Patient Controlled Analgesia (PCA), which is a delivery system for pain medication. This study proposes and demonstrates how to use machine learning and data mining techniques to predict analgesic requirements and PCA readjustment.METHODS: The sample in this study included 1099 patients. Every patient was described by 280 attributes, including the class attribute. In addition to commonly studied demographic and physiological factors, this study emphasizes attributes related to PCA. We used decision tree-based learning algorithms to predict analgesic consumption and PCA control readjustment based on the first few hours of PCA medications. We also developed a nearest neighbor-based data cleaning method to alleviate the class-imbalance problem in PCA setting readjustment prediction.RESULTS: The prediction accuracies of total analgesic consumption (continuous dose and PCA dose) and PCA analgesic requirement (PCA dose only) by an ensemble of decision trees were 80.9% and 73.1%, respectively. Decision tree-based learning outperformed Artificial Neural Network, Support Vector Machine, Random Forest, Rotation Forest, and Na?ve Bayesian classifiers in analgesic consumption prediction. The proposed data cleaning method improved the performance of every learning method in this study of PCA setting readjustment prediction. Comparative analysis identified the informative attributes from the data mining models and compared them with the correlates of analgesic requirement reported in previous works.CONCLUSION: This study presents a real-world application of data mining to anesthesiology. Unlike previous research, this study considers a wider variety of predictive factors, including PCA demands over time. We analyzed PCA patient data and conducted several experiments to evaluate the potential of applying machine-learning algorithms to assist anesthesiologists in PCA administration. Results demonstrate the feasibility of the proposed ensemble approach to postoperative pain management.

Resumo Limpo

ckground appropri postop pain manag contribut earlier mobil shorter hospit reduc cost treatment pain may imped shortterm recoveri detriment longterm effect health studi focus patient control analgesia pca deliveri system pain medic studi propos demonstr use machin learn data mine techniqu predict analges requir pca readjustmentmethod sampl studi includ patient everi patient describ attribut includ class attribut addit common studi demograph physiolog factor studi emphas attribut relat pca use decis treebas learn algorithm predict analges consumpt pca control readjust base first hour pca medic also develop nearest neighborbas data clean method allevi classimbal problem pca set readjust predictionresult predict accuraci total analges consumpt continu dose pca dose pca analges requir pca dose ensembl decis tree respect decis treebas learn outperform artifici neural network support vector machin random forest rotat forest nave bayesian classifi analges consumpt predict propos data clean method improv perform everi learn method studi pca set readjust predict compar analysi identifi inform attribut data mine model compar correl analges requir report previous worksconclus studi present realworld applic data mine anesthesiolog unlik previous research studi consid wider varieti predict factor includ pca demand time analyz pca patient data conduct sever experi evalu potenti appli machinelearn algorithm assist anesthesiologist pca administr result demonstr feasibl propos ensembl approach postop pain manag

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